Tables REF  and REF  show the impact of the size of the Architecture Library used to fit the linear predictor. The tables show how performance varies on a test set of finetuned models for the MobileNetV3 (1.2\(\times \) ) and DONNA search spaces, respectively. Note how the ranking quality, as measured by Kendall-Tau (KT) [1]}, is always better in this work than in DNA [2]}. On top of that, DNA [2]} only ranks models within the search space and does not predict accuracy itself. Another metric to estimate the accuracy predictor's quality is the Mean-Squared-Error (MSE) in terms of predicted top-1 accuracy on the ImageNet validation set. Note that for the MobileNetV3 (1.2\(\times \) ) search space, 20 target accuracies are sufficient for a good predictor, as shown in Table REF . We use the same amount of targets for the EfficientNet-B0, MobilenetV3 (1.0\(\times \) ) and ProxylessNas (1.3\(\times \) ) search spaces. For the DONNA search space, we use 30 target accuracies, see Table REF . Note that the linear accuracy predictor can improve overtime, whenever the Architecture Library is expanded. As predicted Pareto-optimal architectures are finetuned to full accuracy, those results can be added to the library and the predictor can be fitted again using this extra data.
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